A hybrid HMM-neural network with gradient descent parameter training

Hybrid hidden Markov models (HMM) and multi-layer (MLP) neural networks have been applied great success in speech recognition problems. The hybrid system can be applies to sequence classification problems, where multiple looks at an object are used to determine class membership. This presents a utility to perform feature-level fusion in such problems. A new gradient descent algorithm is employed to find optimal parameters within the HMM/MLP model. This scheme has been applied to a data set which contains sonar backscattered signals for four underwater objects for classification as mine-like or non-mine-like.